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BOINC: A Platform for Volunteer Computing


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“Volunteer computing” is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe BOINC’s features, architecture, implementation, and algorithms.
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BOINC: A Platform for Volunteer Computing
David P. Anderson
Received: 21 December 2018 /Accepted: 20 October 2019
#The Author(s) 2019
Abstract Volunteer computingis the use of consum-
er digital devices for high-throughput scientific comput-
ing. It can provide large computing capacity at low cost,
but presents challenges due to device heterogeneity,
unreliability, and churn. BOINC, a widely-used open-
source middleware system for volunteer computing,
addresses these challenges. We describe BOINCsfea-
tures, architecture, implementation, and algorithms.
Keywords BOINC .Volunteer computing .Distributed
computing .Scientific computing .High-throughput
1 Introduction
1.1 Volunteer Computing
Volunteer computing (VC) is the use ofconsumer digital
devices, such as desktop and laptop computers, tablets,
and smartphones, for high-throughput scientific com-
puting. Device owners participate in VC by installing
a program that downloads and executes jobs from
servers operated by science projects. There are currently
about 30 VC projects in many scientific areas and at
many institutions. The research enabled by VC has
resulted in numerous papers in Nature, Science, PNAS,
Physical Review, Proteins, PloS Biology, Bioinformat-
ics, J. of Mol. Biol., J. Chem. Phys, and other top
journals [1].
About 700,000 devices are actively participating in
VC projects. These devices have about 4 million CPU
cores and 560,000 GPUs, and collectively provide an
average throughput of 93 PetaFLOPS. The devices are
primarily modern, high-end computers: they average
16.5 CPU GigaFLOPS and 11.4 GB of RAM, and most
have a GPU capable of general-purpose computing
using OpenCL or CUDA.
The potential capacity of VC is much larger: there are
more than 2 billion consumer desktop and laptop com-
puters [2]. Current models have a peak performance
(including GPU) of over 3 TeraFLOPS, giving a total
peak performance of 6000 ExaFLOPS. The capacity of
mobile devices is similar: there are about 10 billion
mobile devices, and current mobile processors have
on-chip GPUs providing as much as 4 TeraFLOPS [3].
Studies suggest that of people who learn about VC,
between 5% and 10% participate, and that desktop and
mobile devices are available to compute for VC about
60% and 40% of the time respectively [4,5]. Taking
these factors into account, the near-term potential capac-
ity of VC is on the order of hundreds of ExaFLOPS.
The monetary cost of VC is divided between volun-
teers and scientists. Volunteers pay for buying and main-
taining computing devices, for the electricity to power
these devices, and for Internet connectivity. Scientists
pay for a server and for the system administrators,
programmers and web developers needed to operate
the VC project. Operating a typical VC project involves
D. P. Anderson (*)
Space Sciences Laboratory, University of California, Berkeley,
Berkeley, CA 94720, USA
J Grid Computing (2020) 18:99122
/Published online: 16 November 2019
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a few Linux server computers and a part-time system
administrator, costing the research project on the order
of $100 K per year. Several BOINC projects
(Einstein@Home, Rosetta@home, SETI@home) are
of this scale, and they average about 2 PetaFLOPS
throughput each.
How much would this computing power cost on a
commercial cloud? As of October 2019, a compute-
intensive Amazon EC2 instance type (c4.8xlarge)
provides 640 GFLOPS [6], so it would take 3125 such
instances to provide 2 PetaFLOPS. Instances of this type
cost $1.59 per hour [7], for a total cost of $43.52 M per
year 435 times the cost of using VC. The cost is lower
for spotinstances: $0.30 per hour [8], for a total cost
of $8.21 M/year. However, the unreliability of spot
instances may cause problems for long-running jobs.
For projects with GPU applications, AWSs
p3.2xlargeinstance type, with a NVIDIA Tesla
V100 GPU, provides 7.104 TeraFLOPS [9] and costs
$3.06/h, for a total cost of $7.56 M/year. Spot instances
of this node type cost $0.95/h, for a total cost of
$2.34 M/year. In all cases, AWS also charges for data
and storage. Thus, VC is potentially much cheaper than
EC2; the numbers are similar for other cloud providers.
Kondo et al. compared the cost of volunteer and com-
mercial cloud computing in more detail, and reached
similar conclusions [10].
In terms of power consumption, data-center nodes
are typically more efficient (i.e. have greater FLOPS/
watt) than consumer devices. However, to compare
net energy usage, two other factors must be consid-
ered. First, data centers use air conditioning to re-
move the heat generated by hardware; consumer de-
vices generally do not. Second, in cool climates, the
heat generated by consumer devices contributes to
ambient heating, so the net energy cost of computing
may be zero. Thus its not clear whether VC is glob-
ally more or less energy efficient than data-center
computing. In any case, this does not affect VCs
cost to the scientist, since the energy is paid for by
VC is best suited to high-throughput computing
(HTC), where workloads consist of large groups or
streams of jobs, and the goal is high rate of job comple-
tion rather than low job turnaround time. VC is less
suited to workloads that have extreme memory or stor-
age requirements, or for which the ratio of network
communication (i.e. input and output file size) to com-
puting is extremely high.
VC differs from other forms of HTC, such as grids
and clouds, in several ways. Each of these factors pre-
sents challenges that a VC platform must address.
&VCs computers are anonymous, untrusted, inacces-
sible, and uncontrollable. They may misbehave in
any way, and misbehavior cant be stopped or
punished; it must be detected and ignored. In partic-
ular, computers may return (intentionally or not)
incorrect computational results, so the correctness
of these must be validated in some way.
&The computers are heterogeneous in all hardware
and software dimensions [11]. This requires either
multiple application versions or the use of
virtualization. It also complicates the estimation of
job runtimes.
&Creating the resource pool involves recruiting and
retaining volunteers. This requires incentive features
such as teams, computing credit accounting and
screensaver graphics.
&The scale of VC is larger: up to millions of com-
puters and millions of jobs per day. Hence the server
software must be efficient and scalable.
Most VC projects use BOINC, an open-source
middleware system for VC [12]. BOINC lets scientists
create and operate VC projects, and lets volunteers
participate in these projects. Volunteers install an appli-
cation (the BOINC client) and then choose one or more
projects to support. The client is available for desktop
platforms (Windows, Mac, Linux) and for mobile de-
vices running Android. BOINC is designed to compute
invisibly to the volunteer. It runs jobs at the lowest
process priority and limits their total memory footprint
to prevent excessive paging. On mobile devices it runs
jobs only when the device is plugged in and fully
charged, and it communicates only over WiFi.
BOINC can handle essentially all HTC applications.
Many BOINC projects run standard scientific programs
such as Autodock, Gromacs, Rosetta, LAMMPS, and
BLAST. BOINC supports applications that use GPUs
(using CUDA and OpenCL), that use multiple CPUs
(via OpenMP or OpenCL), and that run in virtual ma-
chines or Docker containers.
The development of BOINC began in 2002, and was
carried out by a team at UC Berkeley led by the author,
D. P. Anderson
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with funding from the National Science Foundation.
The first BOINC-based project was launched in 2004.
BOINC is distributed under the open-source LGPL v3
license, but applications need not be open-source.
This paper describes BOINC and how it addresses
the challenges listed above. Section 2describes the
overall structure of BOINC. Section 3focuses on its
job-processing features, while Section 4discusses job
failures and timeouts. Section 5describes the client and
server software. Scheduling is discussed in Section 6.
Mechanisms for incentivizing and supporting volun-
teers are described in Sections 7and 8.Section9pre-
sents related work, and Section 10 offers conclusions
and suggestions for future work.
2 The High-Level Structure of BOINC
BOINCs architecture involves multiple components
interacting through HTTP-based RPC interfaces. It is
designed to be modular and extensible.
Figure 1shows the components of BOINC; these
components are described subsequently. The shaded
boxes represent the BOINC software distribution; un-
shaded boxes represent components developed by third
2.1 Projects and Volunteers
In BOINC terminology, a project is an entity that uses
BOINC to do computing. Each project has a server,
based on the BOINC server software, that distributes
jobs to volunteer devices. Projects are autonomous and
independent. A project may serve a single research
group (SETI@home), a set of research groups using a
common set of applications (,
nanoHUB@home) or a set of unrelated scientists
(IBM World Community Grid, BOINC@TACC).
BOINC provides HTTP-based remote job submission
APIs so that projects can submit and manage jobs using
web-based interfaces (e.g., science gateways) or via
existing systems such as HTCondor.
Projects may operate a public-facing web site. The
BOINC server software provides database-driven web
scripts that support volunteer login, message boards,
profiles, leader boards, badges, and other community
and incentive functions. The project may extend this
with their own web content, such as descriptions and
news of their research. A project is identified by the
URL of its web site.
A volunteer is the owner of a computing device
(desktop, laptop, tablet, or smartphone) who wants to
participate in VC. They do so by a) installing the
BOINC client on the device; b) selecting projects to
support and creating an account on each project, and
c) attaching the BOINC client to each account. Each
attachment can be assigned a resource share indicating
the relative amount of computing to be done, over the
long term, for the project.
2.2 Client/Server Interaction
When attached to a project, the BOINC client periodi-
cally issues RPCs to the projects server to report com-
pleted jobs and get new jobs. The client then downloads
application and input files, executes jobs, and uploads
Fig. 1 The components and RPC interfaces of the BOINC system
BOINC: A Platform for Volunteer Computing 101
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output files. All communication uses client-initiated
HTTP; thus the BOINC client works behind firewalls
that allow outgoing HTTP, and with HTTP proxies. The
server must be publicly accessible via HTTP.
Downloaded files can be compressed in transit; the
client uncompresses them. Files need not be located on
project servers. The validity of downloaded files is
checked using hashes, and (for application files) code
signing; see Section 3.8.
Files are uploaded by an HTTP POST operation,
which is handled by a CGI program on the server.
Upload filenames include a random string to prevent
spoofing. To prevent DoS attacks, projects can option-
ally use encryption-based tokens to limit the size of
uploaded files. File uploads need not go to the project
server; for example, collaborates
with scientists in different countries, and the result files
are sent directly to the collaboratorsservers.
The client also interacts occasionally with a server
operated by the BOINC project itself, to obtain current
lists of projects and of client versions.
All client/server interactions handle failure using ex-
ponential back-off in order to limit the rate of requests
when a server resumes after a period of being off-line.
2.3 Account Managers
The method described above for finding and attaching
projects can be cumbersome when a) the volunteer has
many computers and wants to attach or detach projects
on all of them, or b) the volunteer wants to attach many
To address this, BOINC provides a framework for
web-based account managers (AMs). The BOINC cli-
ent, rather than being explicitly attached to projects, can
be attached to an AM. The client periodically does an
RPC to the AM. The reply includes a list of projects (and
corresponding accounts) to which the client should at-
tach. The client then attaches to these projects and
detaches from others.
The AM architecture was designed to support pro-
ject selectionweb sites, which show a list of projects
with checkboxes for attaching to them. This solves the
two problems described above; for example, a volunteer
with many computers can attach them all to the AM
account, and can then change project attachments across
all the computers with a single AM interaction. Two
such account managers have been developed:
GridRepublic (
BAM! ( More recently,
the AM architecture has been used to implement the
coordinated VC model (Section 10.1), in which
volunteers choose science areas rather than projects.
When a volunteer selects a project on an AM, they
initially have no account on that project. The AM needs
to be able to create an account. To support this, projects
export a set of HTTP RPCs for creating and querying
2.4 Computing and Keyword Preferences
Volunteers can specify computing preferences that
determine how their computing resources are used.
For example, they can enable CPU throttling, in
which computing is turned on and off with a
configurable duty cycle, in order to limit CPU heat.
Other options include whether to compute when the
computer is in use; limits on disk and memory usage;
limits on network traffic; time-of-day limits for com-
puting and network, how much work to buffer, and so
on. Computing preferences can be set through a web
interface on either a project or account manager; they
propagate to all the volunteers computers that are
attached to that account. Preferences can also be set
locally on a computer.
More recently, we have introduced the concept of
keyword preferences. BOINC defines two hierarchies
of keywords: one set for science areas (Physics, As-
trophysics, Biomedicine, cancer research, etc.) and an-
other set for location of the research project (Asia,
United States, U.C. Berkeley, etc.) Volunteers can mark
keywords as yesor no, indicating either that they
prefer to do computing tagged with the keyword, or that
they are unwilling to do it. This system has been used for
two purposes:
&Projects that do research in multiple science areas
can let their volunteers choose what areas to support.
The project assigns keywords to jobs. In selecting
jobs to send to a given volunteer, BOINC will prefer
jobs with yeskeywords, and wontchoosejobs
with nokeywords.
&It enables the coordinated VC model(see
Section 10.1), in which account manager dynam-
ically assigns projects to volunteers in a way that
reflects project keywords and volunteer keyword
D. P. Anderson
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3 Job Processing Abstractions and Features
3.1 Handling Resource Heterogeneity
The pool of volunteered computers is highly diverse
[11]. At a hardware level, computers may have Intel-
compatible, Alpha, MIPS, or ARM CPUs, and their 32-
and 64-bit variants. Intel-compatible CPUs can have
various special instructions like SSE3. ARM processors
may have NEON or VFP floating-point units. The com-
puters run various operating systems (Windows, Mac
OS X, Linux, FreeBSD, Android) and various versions
of these systems. There are a range of GPUs made by
NVIDIA, AMD, and Intel, with various driver versions
offering different features.
BOINC provides a framework that lets projects use
as many of these computing resources as possible, and
to use them as efficiently as possible. Projects can build
versions of their programs for specific combinations of
hardware and software capabilities. These are called app
versions, and the collection of them for a particular
program is called an app. Jobs are submitted to apps,
not app versions.
BOINC defines a set of platforms, which are gener-
ally the combination of a processor type and operating
system: for example, (Windows, Intelx64). Each app
version is associated with a platform, and is sent only
to computers that support that platform.
For finer-grained control of version selection,
BOINC provides the plan class mechanism. A plan class
is a function that takes as input a description of a
computer (hardware and software), and returns a)
whether an app version can run on that computer; b) if
so what resources (CPU and GPU, possibly fractional) it
will use, and c) the peak FLOPS of those resources. For
example, a plan class could specify that an app version
requires a particular set of GPU models, with a mini-
mum driver version. Plan classes can be specified either
in XML or as C++ functions. App versions can option-
ally be associated with a plan class.
App versions have integer version numbers. In gen-
eral, new jobs are processed using the latest version for a
given (platform, plan class) pair; in-progress jobs may
be using older versions.
When the BOINC client requests jobs from a server,
it includes a list of platforms it supports, as well as a
description of its hardware and software. When the
server dispatches a job to a computer, it selects an app
version with a matching platform, and whose plan class
function accepts the computer. The details of this sched-
uling policy are described in Section 6.4.
As an example, SETI@home has 9 supported plat-
forms, 2 apps, 86 app versions, and 34 plan classes.
3.2 Jobs Properties
A BOINC job includes a reference to an app and a
collection of input files. A job has one or more in-
stances. Jobs have input files; instances have output
files. When the server dispatches an instance to a client,
it specifies a particular app version to use. The client
downloads the input files and the files that make up the
app version, executes the main program, and (if it com-
pletes successfully) uploads the resulting output files
and the standard error output.
In the following we use FLOPs(lower-case s) as
the plural of FLOP (floating-point operation). FLOPS
(upper-case s) denotes FLOPs per second.
Each job has a number of properties supplied by the
submitter. These include:
&An estimate of the number of FLOPs performed by
the job. This is used to predict runtime; see
Section 6.3.
&A maximum number of FLOPs, used to abort jobs
that go into an infinite loop.
&An estimate of RAM working-set size, used in serv-
er job selection (see Section 6.4).
&An upper bound on disk usage, used both for server
job selection and to abort buggy jobs that use un-
bounded disk space.
&An optional set of keywords describing the jobs
science area and origin.
3.3 Result Validation
Hosts may return incorrect results because of hardware
errors or malicious user behavior. Projects typically
require that the final results of the job are correct with
high probability. There may be application-specific
ways of doing this: for example, for physical simula-
tions one could check conservation of energy and the
stability of the final configuration. For other cases,
BOINC provides a mechanism called replication-based
validation in which each job is processed on two unre-
lated computers. If the outputs agree, they are accepted
as correct; otherwise a third instance is created and run.
BOINC: A Platform for Volunteer Computing 103
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This is repeated until either a quorum of consistent
instances is achieved, or a limit on the number of in-
stances is reached.
What does it mean for two results to agree? Because
of differences in floating-point hardware and math li-
braries, two computers may return different but equally
valid results. McIntosh et al. [13]showedthatbycare-
fully selecting compilers, compileroptions, and libraries
its possible to get bitwise-identical results for FOR-
TRAN programs across the major platforms. In general,
however, discrepancies will exist. For applications that
are numerically stable, these discrepancies lead to small
differences in the final results. BOINC allows projects to
supply application-specific validator functions that de-
cide if two results are equivalent, i.e. whether corre-
sponding values agree within specified tolerances.
Physical simulations are typically not stable. For such
applications, BOINC provides a mechanism called ho-
mogeneous redundancy [14] in which computers are
divided into equivalence classes, based on their hard-
ware and software, such that computers in the same
class compute identical results for the app. Once a job
instance has been dispatched to a computer, further
instances are dispatched only to computers in the same
equivalence class. BOINC supplies two equivalence
relations: a coarse one involving only OS and CPU
vendor, and a finer-grained one involving CPU model
as well. Projects can define their own equivalence rela-
tions if needed.
In some cases there are discrepancies between app
versions; for example, CPU and GPU versions may
compute valid but incomparable results. To address this,
BOINC provides an option called homogeneous app
version. Once an instance has been dispatched using a
particular app version, further instances use only the
same app version. This can be used in combination with
homogeneous redundancy.
Basic replication-based validation reduces effective
computing capacity by a factor of at least two. BOINC
provides a refinement called adaptive replication that
moves this factor close to one. The idea is to identify
hosts that consistently compute correct results (typically
the vast majority of hosts) and use replication only
occasionally for jobs sent to these hosts. Actually, since
some computers are reliable for CPU jobs but unreliable
for GPU jobs, we maintain this reputationat the
granularity of (host, app version).
Adaptive replication works as follow: the BOINC
server maintains, for each (host, app version) pair, a
count Nof the number of consecutive jobs that were
validated by replication. Once Nexceeds a threshold,
jobs sent to that host with that app version are replicated
only some of the time; the probability of replication goes
to zero as Nincreases. Adaptive replication can achieve
a low bound on the error rate (incorrect results accepted
as correct), even in the presence of malicious volunteers,
while imposing only a small throughput overhead.
Result validation is useful for preventing credit
cheating (Section 7). Credit is granted only to validated
results, and fakedresults generally will fail validation.
For applications that usually return the same answer
(e.g. primality checks), cheaters could simply always
return this result, and would usually get credit. To pre-
vent this, the application can add extra output that de-
pends on intermediate results of the calculations.
3.4 The BOINC Runtime Environment
We now discuss the environment in which the BOINC
client runs project applications, and how the client con-
trols and communicates with running jobs.
The BOINC client installers for Windows and Mac
OS X create an unprivileged user account under which
BOINC runs applications. This provides a measure of
protection against buggy or malicious applications,
since they are unable to read or write files belonging to
existing users.
The BOINC client stores its files in a dedicated
directory. Within this directory, there is a project direc-
tory for each project to which the client is attached,
containing the files associated with that project. There
are also a number of job directories, one for each in-
progress job. Job directories contain symbolic links to
files in the corresponding project directory, allowing
multiple concurrent jobs to share single copies of exe-
cutable and input files.
To enforce volunteer preferences, to support CPU
throttling, and to support the client GUIsmonitoring
and controlfunctions, the client must beable to suspend,
resume, and abort jobs, and to monitor their CPU time
and memory usage. Jobs may consist of a number of
processes. Some modern operating systems provide the
needed process-group operations, but not all do, and
BOINC must run on older systems as well. So BOINC
implements these functions itself, based on message-
passing between the client and the application. This
message-passing uses shared memory, which is support-
ed by all operating systems. There are queues for
D. P. Anderson
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messages from client to application (suspend, resume,
quit) and from application to client (current CPU time,
CPU time at last checkpoint, fraction done, working set
size). Each side polls its incoming queues every second.
All applications run under BOINC must implement
this message protocol. One way to do this is to rebuild
the application for the target platforms, linking it with a
BOINC runtime library, and using its API; we call this a
native application. Another way is to use an existing
executable together with a wrapper program, as de-
scribed in the next section. These two approaches are
shown in Fig. 2.
For sequential programs, the runtime library creates a
timer thread that manages communication with the cli-
ent, while the original worker thread runs the program.
On Windows, the timer thread controls the worker
thread with Windows thread primitives. The Unix
thread library doesnt let one thread suspend another,
so we use a periodic signal, handled by the worker
thread, and suspend the worker by having this signal
handler sleep.
For multithread applications (e.g., OpenMP or
OpenCL CPU) the process structure is a bit different.
On Unix, the program forks. The parent process handles
process control messages, which it implements using
signals. The child process runs the program, and uses a
timer thread to handle status and checkpointing
BOINC supports application-level checkpoint/re-
start. Every few minutes the client sends a message
asking the application to checkpoint. When the ap-
plication reaches a point where it can efficiently
checkpoint (e.g. the start of its outer loop) it writes
a checkpoint file and sends a message to the client
indicating this. Thus the client knows when applica-
tions have checkpointed, and can avoid preempting
jobs that havent checkpointed in a long time, or
In applications that use GPUs, the CPU part of the
program dispatches short kernelsto the GPU. Its
imperative that the application not be suspended while
a kernel is in progress. So the BOINC runtime library
supports masked sections during which suspension and
abortion are deferred. Execution of GPU kernels (as
well as writing checkpoint files) should be done in a
masked section.
By default, the BOINC client runs applications at the
lowest process priority. Non-CPU-intensive and wrap-
per applications are run at normal priority. GPU appli-
cations have been found to perform poorly if run at low
priority, so theyre also run at normal priority.
CPU throttling (see Section 2.4)isimplementedby
having the client suspend and resume the application,
with one-second granularity, and with a duty cycle
determined by the volunteerssetting.
Applications may encounter a transient situation
where they cant continue; for example, a GPU applica-
tion may fail to allocate GPU memory. The BOINC API
provides a temporary exit function that causes the ap-
plication to exit and tells the client to schedule it again
after a certain amount of time. Jobs that do this too many
times are aborted.
Applications should make minimal assumptions
about host software. If they need libraries other than
standard system libraries, they must include them. When
the BOINC client runs a job, it modifies its library
search path to put the job directory first, followed by
the project directory.
Fig. 2 The process structure of
the BOINC runtime environment
BOINC: A Platform for Volunteer Computing 105
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3.5 Application Graphics
App versions can include a second executable program
that generates a visualization of the computation in
progress. The graphics application may be run by the
BOINC screensaver (which cycles among the graphics
of running jobs) or by a command from the BOINC
The graphics application for a job is run in the jobs
directory. It may get state information by reading files in
that directory, or by creating a shared memory segment
shared with the application. Graphics programs typical-
ly use OpenGL for portability.
3.6 Wrapped and Virtual Machine Applications
Native applications must be modified to use the BOINC
API, and then rebuilt for the target platforms. This may
be difficult or impossible, so BOINC provides wrap-
permechanisms for using existing executables.
The BOINC wrapper allows arbitrary executables to
be run under BOINC, on the platforms for which they
were built. The wrapper acts as an intermediary between
the BOINC client and the application.It handles runtime
messages from the client (Section 3.4) and translates
them into OS-specific actions (e.g. signals on Unix) to
control the application. BOINC provides versions of the
wrapper for the major platforms. An app version in-
cludes the appropriate version of the wrapper (as the
main program), and the application executable. The
wrapper can be instructed to run a sequence of execut-
ables. Marosi et al. developed a more general wrapper
that supports arbitrary workflows using a shell-like
scripting language [15].
BOINC supports applications that run in virtual ma-
chines using VirtualBox, a multi-platform open-source
VM system for Intel-compatible computers. Applica-
tions can then be developed on Linux and run on Win-
dows and Mac OS X computers with no additional
work. The BOINC client detects and reports the pres-
ence of VirtualBox. VM apps are only dispatched to
hosts where VirtualBox is installed. On Windows, the
recommended BOINC installer installs VirtualBox as
To support VM apps, BOINC provides a VBox
wrapper program, which interfaces between the BOINC
client and the VirtualBox executive. It translates runtime
system messages to VirtualBox operations. VirtualBox
supports sharing of directories between host and guest
systems. The VBox wrapper creates a shared directory
for VM jobs, places input files there, and retrieves
output files from there.
In addition to reducing heterogeneity problems, VM
technology provides a strong security sandbox, allowing
untrusted applications to be used. It also provides an
application-independent checkpoint/restart capability,
based on the VirtualBox snapshotfeature. The wrap-
per tells VirtualBox to create a snapshot every few
minutes. If computing is stopped (e.g. because the host
is turned off) the job can later be restarted, on that host,
from the most recent snapshot.
BOINCs VM system can be used together with
container systems like Docker [16]. Docker-based ap-
plications consist of a Docker image (the OS and library
environment of choice) and an executable to run in the
container. The container is run within a VirtualBox
virtual machine. Docker images consist of a layered
set of files. Typically only the top layer changes when
new app versions are released, so network traffic and
storage requirements are smaller than when using mono-
lithic VM images.
VM apps normally include the VM or Docker image
as part of the app version, but its possible to have it be
an input file of the job, in which case a single (BOINC)
app can be used for arbitrarily many science applica-
tions. This is significant because creating an app in-
cludes signing its files, which is a manual operation.
We call such an app a universal app. If a projects
science applications are already packaged as VM or
Docker images, they can be run under BOINC with no
porting or other work.
There is a security consideration in the use of univer-
sal apps, since job input files are not code-signed (see
Section 3.8). If a projects server is hacked, the hackers
could substitute a different VM image. However, the
danger is small since the VM is sandboxed and cant
access files on the host other than its own input and
output files.
VM apps were proposed in 2007 by González et al.
[17], and VM wrappers were developed at CERN [18]
and SZTAKI [19]. Ferreira et al. describe a wrapper able
to interface to multiple VM hypervisors [20].
3.7 Job Submission and File Management
Job submission consists of three steps: 1) staging the
jobs input files to a publicly-accessible web server, 2)
creating the job, and 3) handling the results of a
D. P. Anderson
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completed job. BOINC supplies C++ APIs and
command-line tools for staging files and submitting jobs
on the server. It also provides HTTP RPC APIs for
remote file management and job submission, and it
supplies bindings of these interfaces in Python, PHP,
and C++. These interfaces are defined in terms of
batchesof jobs, and are designed for efficiency: for
example, submitting a batch of a thousand jobs takes
less than a second.
The handling of completed jobs is done by a per-app
assimilator daemon. This is linked with a project-
supplied C++ or Python function that handles a com-
pleted job. This function might, for example, move the
output files to a different directory, or parse the output
files and write the results to a database.
These mechanisms make it straightforward to use
BOINC as a back end for other job processing systems.
For example, we implemented a system where
HTCondor jobs can be automatically routed to a BOINC
project. The mechanisms can also be used for web-
based remote job submission systems. For example, a
science gateway might automatically route jobs, submit-
ted via the web, to either local compute nodes or a
BOINC server (see Section 10.2).
In some BOINC projects, multiple job submitters
contend for the projects resources. BOINC provides
an allocation system that handles this contention in a
way thats fair to submitters both with sporadic and
continuous workloads. For each submitter, the system
maintains a balance that grows linearly at a particular
rate, up to a fixed maximum. The rate may differ be-
tween submitters; this determines the average comput-
ing throughput available to each submitter. When a
submitter uses resources, their balance is decreased ac-
cordingly. At any given point, the jobs of the submitter
with the greatest balance are given priority. We call this
the linear-bounded model; BOINC uses it in other con-
texts as well (Sections 6.1 and 10.1). Given a mix of
continuous and sporadic workloads, this policy priori-
tizes small batches, thereby minimizing average batch
3.8 Storage
The BOINC storage model is based on named files.
Files are immutable: a file with a given name cannot
change. Projects must enforce this. Input and output
files also have logical names, by which applications
refer to them; these need not be unique.
The entities involved in computation app ver-
sions and jobs consist of collections of files. The
files in an app version must be code-signed by tagging
them with a PKE-encrypted hash of the file contents.
The recommended way of doing this is to keep the
private key on a computer that is never connected to a
network and is physically secure. Files are manually
copied to this computer say, on a USB drive and
signed. This procedure provides a critical level of
security: hackers are unable to use BOINC to run
malware on volunteer hosts, even if they break into
project servers.
Job input and output files normally are deleted from
the volunteer host after the job is finished. They can be
marked as sticky, in which case they are not deleted.
Scheduler requests include a list of sticky files on the
client, and replies can direct the client to delete particu-
lar sticky files.
Files making up app versions are automatically sticky
in the sense that they are deleted only when the app
version has been superseded by another app version
with the same app, platform, and plan class, and with a
greater version number.
The amount of storage that BOINC may use on a
given host is limited by its free disk space, and also by
volunteer preferences (which can specify, e.g., mini-
mum free space or maximum fraction of space used
by BOINC). Scheduler requests include the amount of
usable space, and the scheduler doesntsendjobs
whose projected disk usage would exceed this. If the
amount of usable space is negative (i.e. BOINC has
exceeded its limit) the scheduler returns a list of sticky
files to delete.
If the host is attached to multiple projects and the
disk limit is reached, the projects are contending for
space. The client computes a storage share for each
project, based on its resource share and the disk usage
of the projects; this is what is conveyed to the
Files need not be associated with jobs. A scheduler
reply can include lists of arbitrary files to be
downloaded, uploaded, or deleted. Thus BOINC can
be used for volunteer storage as well as computing. This
might be used for a variety of purposes: for example, to
cache recent data from a radio telescope so that, if an
event is detected a few hours after its occurrence, the
raw data would still be available for reanalysis. It can
also be used for distributed data archival; see
Section 10.3.
BOINC: A Platform for Volunteer Computing 107
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3.9 The Anonymous Platform Mechanism
By default, the BOINC client gets application execut-
ables from the project server. This model doesntad-
dress the following scenarios:
&The volunteers computer type is not supported by
the project.
&The volunteer wants, for security reasons, to run
only executables they have compiled from source.
&The volunteer wants to optimize applications for
particular CPU models, or to make versions for
GPUs or other coprocessors.
To handle these cases, BOINC offers a mechanism
called anonymous platform. This lets volunteers build
applications themselves, or obtain them from a third
party, rather than getting them from the project server.
This mechanism can be used only for projects that make
their application source code available.
After building their own version of projectsapplica-
tions, the volunteer creates a configuration file describ-
ing these versions: their files, their CPU and GPU usage,
and their expected FLOPS. If the BOINC client finds
such a configuration file, it conveys it to the scheduler,
which uses that set of app versions in the job dispatch
process. Projects that support anonymous platform typ-
ically supply input and output files for a set of reference
jobsso that volunteers can check the correctness of
their versions.
In some projects (for example, SETI@home) the
anonymous platform mechanism has enabled a commu-
nity of volunteers who have made versions for various
GPUs and vendor-specific CPU features, and who have
made improvements in the base applications. Many of
these versions have later been adopted by the project
3.10 Specialized Job Processing Features
App versions are assigned version numbers that increase
over time. In general, BOINC always uses the latest
version for a given (platform, plan class) combination.
In some cases a project may release a new app version
whose output is different from, and wont validate
against, the previous version. To handle this, BOINC
allows jobs to be pinnedto a particular version num-
ber, in which case they will be processed only by app
versions with that version number.
Some BOINC projects have jobs that run for weeks
or months on typical computers [21]. Because of host
churn or user impatience, some of these jobs dontrunto
completion. The intermediate outputs of these jobs may
have scientific value. BOINC has two features to sup-
port such jobs. First, applications can tell the client thata
particular output file is complete and can be uploaded
immediately, rather than waiting for the job to finish.
Second, the application can generate trickle-up mes-
sages that are conveyed immediately to the server and
handled by project-specific logic. This can be used, for
example, to give volunteers credit for partial job
Some applications have large input files, each of
which is used by many jobs. To minimize network
traffic and server load for these applications, BOINC
has a feature called locality scheduling. The large input
files are marked as sticky (see Section 3.8)sothatthey
remain resident on the host. The scheduler RPC request
message includes a list of sticky files on the host. The
scheduler preferentially sends jobs that use these files
(see Section 6.4).
BOINC allows jobs to be targeted at a particular host
or volunteer. This can be used, for example to ensure
that test jobs are executed on the projectsown
The difference in performance between volunteer
devices say, a phone versus a desktop with a high-
end GPU can be several orders of magnitude. A job
that completes in 5 min on one could take a week on the
other. To reduce server load and maintain volunteer
interest, its desirable that job runtime be on the order
of an hour. To support uniformity of job runtime across
platforms, BOINC provides a mechanism that lets pro-
jects create jobs in a range of sizes (say, processing
smaller or larger amounts of data). The BOINC server
maintains statistics of the performance of each (host, app
version) pair and dispatches jobs to appropriate hosts
based on their size larger jobs are sent to faster hosts.
Applications that use negligible CPU time (e.g., sen-
sor monitoring, network performance testing, and web
crawling) can be labeled as non-CPU-intensive. BOINC
treats these applications specially: it runs a single job per
client, it always runs the application, and it runs it at
normal process priority.
Debugging BOINC applications e.g. figuring out
why they crash is challenging because the errors occur
on remote, inaccessible machines. BOINC provides fea-
tures to assist in this. The client reports exit codes and
D. P. Anderson
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signal numbers, as well as standard error output, to the
server. The server provides web interfaces allowing
developers to break down errors by platform, app ver-
sion, and error type, and to see the details of devices
where errors occurred; this facilitates finding problems
specific to a particular OS version or video device
4 The Life of a Job
In most batch systems the life of a job is simple: it gets
dispatched toa node and executed. In BOINC, however,
a dispatched job has various possible outcomes:
1. The job completes successfully and correctly.
2. The program crashes.
3. The job completes but the returned results are in-
correct, either because of hardware malfunction or
because a malicious volunteer intentionally substi-
tutes wrong results.
4. The job is never returned, because (for example) the
computer stops running BOINC.
To ensure the eventual completion of a job, and to
validate its results, it may be necessary to create addi-
tional instances of the job.
Each job Jis assigned a parameter delay_bound(J) by
its submitter. If an instance of Jis dispatched to a host at
time T, the deadline for its completion is T+
delay_bound(J). If the completed results have not been
returned to the server by the deadline, the instance is
assumed to be lost (or excessively late) and a new
instance of Jis created. The scheduler tries to dispatch
jobs only to hosts that are likely, based on runtime
estimates and existing queue sizes, to complete them
by their deadlines.
Projects may have time-sensitive workloads: e.g.,
batches of jobs that should be finished quickly because
the submitter needs the results to proceed. Such jobs
may be assigned low delay bounds; however, this may
limit the set of hosts to which the job can be dispatched,
which may have the opposite of the desired effect.
For workloads that are purely throughput-oriented,
delay_bound(J) can be large, allowing even slow hosts
to successfully finish jobs. However, it cantbearbi-
trarily large: while the job is in progress, its input and
output files take up space on the server, and
delay_bound(J) limits this period. For such workloads,
a delay bound of a few weeks may be appropriate.
In addition to delay_bound(J), a job has the follow-
ing parameters; these are assigned by the submitter,
typically at the level of app rather than job:
&min_quorum(J): validation is done when this many
successful instances have completed. If a strict ma-
jority of these instance have equivalent results, one
of them is selected as the canonical instance, and
that instance is considered the correct result of the
&init_ninstances(J): the number of instances to create
initially. This must be at least min_quorum(J);it
may be more in order to achieve a quorum faster.
&max_error_instances(J): if the number of failed in-
stances exceeds this, the job as a whole fails and no
more instances are created. This protects against
jobs that crash the application.
&max_success_instances(J): if the number of suc-
cessful instances exceeds this and no canonical in-
stance has been found, the job fails. This protects
against jobs that produce nondeterministic results.
So the life of a job Jis as follows:
&Jis submitted, and init_ninstances(J) instances are
created and eventually dispatched.
&When an instances deadline is reached and it
hasnt been reported, the server creates a new
&When a successful instance is reported and there is
already a canonical instance, the new instance is
validated against it to determine whether to grant
credit (see Section 7). Otherwise, if there are at least
min_quorum(J) successful instances, the validation
process is done on these instances. If a canonical
instance is found, the apps assimilator is invoked to
handle it. If not, and the number of successful in-
stances exceeds max_success_instances(J),Jis
marked as failing.
&When a failed instance is reported, and the number
of failed instances exceeds max_error_instances(J),
Jis marked as failing. Otherwise a new instance is
When a canonical instance is found, Js input files
and the output files of other instances can be deleted,
and any unsent instances are cancelled.
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Its possible that a successful instance of Jis reported
after a canonical instance is found. Its important to
grant credit to the volunteer for the instance, but only
if its correct. Hence the output files of the canonical
instance are retained until all instances are resolved.
After all instances are resolved, and the job has
succeeded or failed, the job and job instance records
can be purged from the database. This keeps the data-
base from growing without limit over time; it serves as a
cache of jobs in progress, not an archive of all jobs.
However, the purging is typically delayed for a few days
so that volunteers can view their completed jobs on the
5 Software Architecture
5.1 Server Architecture
A BOINC project operates a server consisting of one or
more computer systems running the BOINC server soft-
ware. These systems may run on dedicated hardware,
VMs, or cloud nodes. The server has many interacting
processes, as shown in Fig. 3.
A BOINC server centers on a relational database
(usually MySQL or MariaDB). The database includes
tables for most of the abstractions mentioned earlier:
volunteer accounts, hosts, apps, app versions, jobs, job
instances, and so on. To reduce DB load, details such as
the list of a jobs input files are stored in XML blobs
rather than in separate tables.
Client RPCs are handled by a scheduler, which is
implemented as a CGI or FCGI program run from a
web server. Typically many RPCs are in progress at
once, so many instances of the scheduler are active.
The main function of the scheduler is to dispatch job
instances to clients, which requires scanning many
jobs and job instances. Doing this by directly query-
ing the database would limit performance. Instead,
BOINC uses an architecture in which a shared-
memory segment contains a cache of job instances
available for dispatch typically on the order of a
thousand jobs. This cache is replenished by a feeder
process, which fetches unsent job instances from the
database and puts them in vacant slots in the cache.
The feeder supports the homogeneous redundancy,
homogeneous app version, and job size features by
ensuring that the shared-memory cache contains job
of all the different categories.
An instance of the scheduler, rather than querying the
database for jobs to send, scans the job cache. It acquires
a mutex while doing this; the scan is fast so there is little
contention for the mutex. When the scheduler sends a
job, it clears that entry in the cache, and the entry is
eventually refilled by the feeder. The scheduler updates
the database to mark the job instance as sent. The
efficiency of this mechanism allows a BOINC server
even on a single machine to dispatch hundreds of jobs
per second [22].
The servers other functions are divided among a set
of daemon processes, which communicate and synchro-
nize through the database.
Fig. 3 The components of a
BOINC server
D. P. Anderson
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&Validato r: There is one for each app that uses rep-
lication. It examines the instances of a job, compares
their output files, decides whether there is a quorum
of equivalent results, and if so designates one in-
stance as canonical. For apps that use homoge-
neous redundancy to achieve bitwise agreement be-
tween instances, BOINC supplies a validator that
compares the output files byte for byte. Other apps
use custom validators with a project-supplied func-
tion that does the (fuzzy) comparison for that app.
&Assimilator: This handles completed and validated
jobs. There is one assimilator per app; it includes a
project-supplied handler function. This function
might move the output files to a particular destina-
tion, or parse them and write the results to a
&Tran s i tioner: Viewing the progress of a job as a
finite-state machine, this handles the transitions. The
events that trigger transitions come from potentially
concurrent processes like schedulers and validators.
Instead of handling the transitions, these programs
set a flag in the jobs database record. The
transitioner enumerates these records and processes
them. This eliminates the need for concurrency con-
trol of DB access.
&File deleter: This deletes input and output files of
completed and assimilated jobs.
&Database purger: This deletes the database records
of completed jobs and job instances.
This multi-daemon architecture provides a form of
fault-tolerance. If a daemon fails (say, an assimilator
fails because an external database is down) the other
components continue; work for the failed component
accumulates in the database and is eventually
A BOINC server is highly scalable. First, the dae-
mons can be run on separate hosts. Second, all of the
server functions except the database server can be di-
vided among multiple processes, running on the sameor
on different hosts, by partitioning the space of database
job IDs: if there are Ninstances of a daemon, instance i
handles rows for which (ID mod N) = i.Incombination
with the various options for scaling database servers,
this allows BOINC servers to handle workload on order
of millions of jobs per day.
The BOINC server software also includes scripts and
web interfaces for creating projects, stopping/starting
projects, and updating apps and app versions.
5.2 Client Architecture
The BOINC client software consists of three programs,
which communicate via RPCs over a TCP connection:
&The core client manages job execution and file
transfers. The BOINC installer arranges for the cli-
ent to run when the computer starts up. It exports a
set of RPCs to the GUI and the screensaver.
&A GUI (the BOINC Manager) lets volunteers con-
trol and monitor computation. They can, for exam-
ple, suspend and resume CPU and GPU computa-
tion, and control individual jobs. They can view the
progress and estimated remaining time of jobs, and
can view an event log showing a configurable range
of internal and debugging information.
&An optional screensaver shows application graphics
(Section 3.5) for running jobs when the computer is
Third party developers have implemented other
GUIs; for example, BoincTasks is a GUI for Windows
that can manage multiple clients [23]. For hosts without
a display, BOINC supplies a command-line program
providing the same functions as the Manager.
The core client is multi-threaded. The main thread
uses the POSIX select() call to multiplex concurrent
activities (network communication and time-
consuming file operations like decompression and
checksumming). Each GUI RPC connection is serviced
by a separate thread. All the threads synchronize using a
single mutex; the main thread releases this mutex while
its waiting for I/O or doing a file operation. This ensures
rapid response to GUI RPCs.
6 Scheduling
Job scheduling is at the core of BOINC. The scheduling
system consists of three related parts (see Fig. 4):
&The BOINC client maintains a queue of jobs; these
jobs use various combinations of CPUs and GPUs.
Resource scheduling is the decision of what jobs to
run at a given point.
&Work fetch involves deciding when to request more
jobs from a project, how much work to request, and,
if the client is attached to multiple projects, which
project to ask.
BOINC: A Platform for Volunteer Computing 111
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&Job selection is the servers choice of what jobs to
send in response to a client work request.
The scheduling system has several goals:
&Eventually complete all jobs.
&Maximize throughput by a) avoiding idle processing
resources; b) completing jobs by their deadline,
thereby avoiding creating additional instances of
the job; c) using the best-performing app version
for a given job and host.
&Enforce the volunteers per-project resource shares
and computing preferences.
The performance of early versions of the system have
been studied using simulation [22,24,25,26]. Howev-
er, the current system has for the most part been devel-
oped empirically, in response to project demands.
BOINCs scheduling policies revolve around esti-
mating the runtime of a job, on a particular host, using
a particular app version. There are actually two notions
of runtime:
&The raw runtime is how long the job takes running
full-time on the host.
&The scaled runtime is how long it takes given a)
CPU throttling (Section 2.4) and b) the possibly
sporadic availability of the processing resources
used by the app version (resources are unavailable
when the computer is turned off or their use is
disallowed by user preferences). The client main-
tains averages of the availability of CPUs and GPUs,
and reports these to the server.
Runtime estimates are intrinsically uncertain.
BOINC tries to make good estimates, and to deal grace-
fully with situations where the estimates are inaccurate.
6.1 Client: Resource Scheduling
On a given host, the client manages a set of processing
resources: a CPU and possibly one or more coproces-
sors such as GPUs. There may be multiple instances of
each processing resource. Volunteer preferences may
limit the number of available CPU instances, denoted
n_usable_cpus. The client estimates the peak FLOPS of
each instance. For CPUs this is the Whetstone bench-
mark result; for GPUs its the vendor-supplied estimate
based on number of cores and clock rate.
At a given point the client has a queue of jobs to run.
A given job may be a) not started yet; b) currently
executing; c) in memory but suspended, or d) preempted
and not in memory.
Each job Jhas a fixed resource usage: the number
(possibly fractional) of instances of each processing
resource that it uses while executing. For CPUs, frac-
tional resource usage refers to time: 0.5 means that Jhas
1 thread that runs half the time. For GPUs, fractional
usage typically refers to core usage: 0.5 means that J
uses half the GPUs cores. In either case, throughput is
maximized by running 2 such jobs at once.
Each job Jhas an estimated RAM working set size
est_wss(J). If Jis running or preempted this is the latest
working set size reported by the operating system. If Jis
unstarted and theres a running job J
using the same app
version, est_wss(J) is the working set size of J
wise its the project-supplied RAM usage estimate.
We call a set of jobs feasible if a) for each coproces-
sor, the sum of usages by the jobs is at most the number
of instances; b) the sum of CPU usages of CPU jobs is at
most n_usable_cpus, and the sum of CPU usages of all
jobs is at most n_usable_cpus + 1;c)thesumof
est_wss(J) is at most the amount of available RAM. A
set of jobs is maximal if its feasible and adding any
other queued job would make it infeasible. The client
always runs a maximal set of jobs.
The basic resource scheduling policy is weighted
round robin (WRR). Projects have dynamic scheduling
Fig. 4 Job scheduling in BOINC involves three interacting poli-
cies: resource scheduling, work fetch, and job selection
D. P. Anderson
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priorities based on their resource share and recent usage,
using the linear bounded model described in
Section 3.7. A maximal set of jobs from the highest-
priority projects are run FIFO. At the end of each time
slice (default 1 h) priorities are recomputed and a possi-
bly different maximal set of jobs is run. Time-slicing is
used so that volunteers see a mixture of projects.
The WRR policy can result in jobs missing their
deadlines. To avoid this, we need a more sophisticated
policy that predicts and avoids deadline misses. This
requires estimating the remaining runtime of jobs. If a
job hasntstartedyetthisisthejobsFLOPestimate
(Section 3.2) divided by the server-supplied FLOPs/s
estimate (Section 6.3); we call this the static estimate. If
the job has started and has reported a fraction done (see
Section 3.4) this gives a dynamic estimate, namely the
current runtime divided by the fraction done. For some
applications (e.g. those that do a fixed number of itera-
tions of a deterministic loop) the dynamic estimate is
accurate; projects can flag such applications, instructing
the client to always use the dynamic estimate. For other
applications the fraction done is approximate; in this
case the client uses a weighted average of the static
and dynamic estimates, weighted by the fraction done.
The client periodically simulates the execution of all
jobs under the WRR policy, based on the estimated
remaining scaled runtimes of the jobs. This simulation
(described in more detail below) predicts which jobs
will miss their deadlines under WRR.
We can now describe BOINCsresourcescheduling
policy. The following steps are done periodically, when
a job exits, and when a new job becomes ready to
&Do the WRR simulation.
&Make a list of jobs ordered by the following criteria
(in descending priority): a) prefer jobs that miss their
deadline under WRR simulation, and run these
earliest deadline first; b) prefer GPU jobs to CPU
jobs; c) prefer jobs in the middle of a time slice, or
that havent checkpointed; d) prefer jobs that use
more CPUs; e) prefer jobs from projects with higher
scheduling priority.
&Scan through this list, adding jobs until a maximal
set is found. Run these jobs, and preempt running
jobs not in the set.
In essence, BOINC uses WRR scheduling unless
there are projected deadline misses, in which case it uses
earliest deadline first (EDF). EDF is optimal on
uniprocessors but not multiprocessors [27]; a policy
such as least slack time first might work better in some
6.2 Client: Work Fetch Policy
Volunteer preferences include lower and upper limits,
denoted B
and B
on the amount of buffered work,
as measured by remaining estimated scaled runtime.
When the amount of work for a given processing re-
source falls below B
, the client issues a request to a
project, asking for enough work to bring the buffer up to
at least B
This buffering scheme has two purposes. First, the
client should buffer enough jobs to keep all processing
resources busy during periods when no new jobs can be
fetched, either because the host is disconnected from the
Internet, project servers are down, or projects have no
jobs. B
should reflect the expected duration of these
periods. Second, the frequency of scheduler RPCs
should be minimized in order to limit load on project
servers; an RPC should get many jobs if possible. The
difference B
expresses this granularity.
The WRR simulation described above also serves to
estimate buffer durations; see Fig. 5.
Fig. 5 Example of WRR
simulation for a given processing
resource. Dark gray bars represent
instance usage. The area of light
gray bars is the resources
BOINC: A Platform for Volunteer Computing 113
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In the simulation, each instance Aof the processing
resource Ris used for some period T(A) starting at the
current time. If T(A) < B
for any instance, then the
buffer for that resource needs to be replenished.
Shortfall(R) denotes the minimum additional job dura-
tion needed to bring T(A) up to at least B
for all
instances A,namely
shortfall RðÞ¼
instances A of R
max 0;BHI TAðÞðÞ
A work request includes several components for each
processing resource R:
&req_runtime(R): the buffer shortfall. The server
should send a set of jobs whose scaled runtime
estimates times the resource usage exceeds this.
&req_idle(R): The number of idle instances. The serv-
er should send jobs whose total usage of Ris at least
&queue_dur(R): the estimated remaining scaled
runtime of queued jobs that use R.Thisisusedto
estimate the completion time of new jobs
(Section 6.4).
There are situations in which a work request cantbe
issued to a project P: for example, Pis suspended by the
user, or the client is in exponential backoff from a
previous failed RPC to P. There are also situations
where work for Rcant be requested from P: for exam-
ple, if it has no app versions that use R,oruserprefer-
ences prohibit using R. If none of these conditions hold,
we say that Ris fetchable for P.
We can now describe the work fetch policy. First,
perform the WRR simulation. If some resource needs to
be replenished, scan projects in order of decreasing
scheduling priority (Section 6.1). Find the first one P
for which some processing resource Rneeds to be
replenished, and Ris fetchable for P.Issueascheduler
RPC to P, with work requests parameters as described
above for all processing resources that are fetchable for
Sometimes the client makes a scheduler RPC to a
project Pfor reasons other than work fetch - for exam-
ple, to report a completed job instance, because of a user
request, or because an RPC was requested by the pro-
ject. When this occurs, the client may piggybacka
work request onto the RPC. For each processing re-
source type R, it sees if P is the project with highest
scheduling priority for which Ris fetchable. If so, it
requests work for Ras described above.
The client doesnt generally report completed jobs
immediately; rather, it defers until several jobs can be
reported in one RPC, thus reducing server load. How-
ever, it always reports a completed job when its deadline
is approaching or if the project has requested that results
be reported immediately.
6.3 Server: Job Runtime Estimation
We now turn to the servers job selection policy. Job
runtime estimation plays two key roles in this. First, the
scheduler uses runtime estimates to decide how many
jobs satisfy the clients request. Second, it uses the
estimated turnaround time (calculated as runtime plus
queue duration) to decide whether the job can be com-
pleted within its delay bound.
The job submitter may have a priori information
about job duration; for example, processing a data file
thats twice as large as another might be known to take
twice as long. This information is expressed in a param-
eter est_flop_count(J) supplied by the job submitter (see
Section 3.2). This is an estimate of the number of FLOPs
the job will use, or more precisely TR,whereTis the
expected runtime of the job on a processing resource
whose peak FLOPs/s is R. If job durations are unpre-
dictable, est_flop_count() can be a constant.
The BOINC server maintains, for each pair of host H
and app version V, the sample mean R̅(H, V) and vari-
ance of (runtime(J)/est_flop_count (J)) over all jobs J
processed by Hand V. It also maintains, for each app
version V, the sample mean R̅(V) of (runtime(J)/
est_flop_count (J)) for jobs processed using Vacross
all hosts.
For a given host Hand app version V, the server
computes a projected FLOPS proj_flops(H, V), which
can be thought of as the estimated FLOPS, adjusted for
systematic error in est_flop_count(). proj_flops(H, V) is
computed as follows.
&If the number of samples of R̅(H, V) exceeds a
threshold (currently 10) then proj_flops(H, V) is
R̅(H, V).
&Otherwise, if the number of samples of R̅(V) ex-
ceeds a threshold, proj_flops(H, V) is R̅(V).
&Otherwise, proj_flops(H, V) is the peak FLOPS of V
running on H, i.e. the sum over processing resources
Rof VsusageofRtimes the peak FLOPS of R.
D. P. Anderson
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The estimated raw runtime of job Jon host Husing
app version V, denoted est_runtime(J, H, V), is then
est_flop_count(J)/proj_flops(H, V).proj_flops(H, V) is
also used to select which app version to use to process J
on H, as explained below.
6.4 Server: Job Dispatch Policy
A scheduler RPC request message from a host Hin-
cludes a description of H, including its OS, processing
resources (CPUs and GPUs), memory and disk. It also
includes, for each processing resource R, the fraction of
time R is available for use, and the request parameters
described in Section 6.2.
The RPC reply message includes a list of jobs, and
for each job a) the app version to use, and b) an estimate
of the FLOPS that will be performed by the program.
The general goal of the scheduler is to dispatch jobs that
satisfy the request, and to use the app versions that will
run fastest.
The scheduler loops over processing resources, han-
dling GPUs first so that theyll be kept busy if the disk
space limit is reached (as it dispatches jobs, the sched-
uler keeps track of how much disk space is still avail-
able). For each processing resource R, the scheduler
scans the job cache, starting at a random point to reduce
lock conflict, and creates a candidate list of possible jobs
to send, as follows.
For each scanned job J, we find the app versions that
a) use a platform supported by H;b)useR;c)pass
homogeneous redundancy and homogeneous app ver-
sion constraints, and d) pass the plan class test. If no
such app version exists, we continue to the next job.
Otherwise we select the app version Vfor which
proj_flops(H, V) is greatest.
We then compute a score for J, representing the
valueof sending it to H. The score is the weighted
sum of several factors:
&If Jhas keywords (see Section 2.4), and the volun-
teer has keyword preferences, whether Jskeywords
match the volunteersyeskeywords; skip Jif it
has a nokeyword.
&The allocation balance of the job submitter (see
Section 3.7).
&Whether Jwas skipped in handling a previous
scheduler request. The idea is that if Jis hard to
send, e.g. because of large RAM requirements, we
should send it while we can.
&If the app uses locality scheduling, whether Hal-
ready has the input files required by J(see
Section 3.10).
&If the app has multi-size jobs, whether proj_flops(H,
V) lies in the same quantile as the size of J(see
Section 3.10).
We then scan the candidate list in order of descending
score. For each job J, we acquire a semaphore that
protects access to the job cache, and check whether
another scheduler process has already dispatched J,
and skip it if so.
We the n d o a fast check(with no database ac-
cess) to see whether we can actually send Jto H,given
the jobs we have already decided to send. We skip Jif
a) the remaining disk space is insufficient; b) Jprob-
ably wont complete by its deadline: i.e. if
queue_dur(R) + est_runtime(J, H, V) >
delay_bound(J); or c) we are already sending another
instance of the same job as J.
We then flag Jas takenin the job cache, release the
mutex, and do a slow checkinvolving database ac-
cess: Have we already sent an instance of Jto this
volunteer? Has Jerrored out since we first considered
it? Does it still pass the homogeneous redundancy
check? If so, skip Jand release it in the cache.
If we get this far, we add the pair (J, V) to the list of
jobs to send to client, and free the slot in the job cache.
Letting Ebe the estimated scaled runtime of the job, we
add Eto queue_dur(R),subtractEfrom req_runtime(R),
and subtract J
susageofRfrom req_idle(R).Ifbothof
the latter are nonpositive, we move on to the next
processing resource. Otherwise we move to the next
job in the candidate list.
BOINC grants credit an estimate of FLOPs performed
for completed jobs. The total and exponentially-
weighted recent average credit is maintained for each
computer, volunteer, and team. Credit serves several
purposes: it provides a measure of progress for individ-
ual volunteers; it is the basis of competition between
volunteers and teams; it provides a measure of compu-
tational throughput for projects and for BOINC as a
whole; and it can serve as proof-of-work for virtual
currency systems.
BOINC: A Platform for Volunteer Computing 115
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
To support these uses, the credit system ideally
&be device neutral: similar jobs (in particular the
instances of a replicated job) should get about the
same credit regardless of the host and computing
resource on which they are processed;
&be project neutral: a given device should get about
the same credit per time regardless of which project
it computes for;
&resist creditcheating: i.e. efforts to get credit for jobs
without actually processing them.
The basic formula is that one unit of credit represents
1 day of a CPU with a Whetstone benchmark of 1
GFLOPS. Credit is assigned in different ways depend-
ing on the properties of the application. If all jobs do the
same computation, the project can time the job on a
typical computer with known Whetstone benchmark,
compute the credit accordingly, and tell BOINC to grant
that amount per job. Similarly, if the app consists of a
loop that executes an unpredictable number of times but
that always does the same computation, the credit per
iteration can be computed ahead of time, and credit can
be granted (in the validator) based on the number of loop
iterations performed.
For applications not having these properties, a differ-
ent approach is needed. The BOINC client computes the
peak FLOPS of processing resources as described earli-
er. The actual FLOPS of a particular application will be
lower because of memory access, I/O, and synchroniza-
tion. The efficiency of the application on a given com-
puter is actual FLOPS divided by peak FLOPS. The
efficiency of a CPU application can vary by a factor of 2
between computers; hence computing credit based on
runtime and benchmarks would violate device neutrali-
ty. The problem is worse for GPUs, for which efficien-
cies are typically much less than for CPUs, and vary
more widely.
To handle these situations, BOINC uses an adaptive
credit system. For a completed job instance J,wedefine
its peak FLOP count PFC(J) as:
runtime JðÞ*usage rðÞ*peak flops rðÞ
where Ris the set of processing resources used by the
app version.
The server maintains, for each (host, app version)
pair and each app version, the statistics of PFC(J)/
est_flop_count(J) where est_flop_count(J) is the a priori
job size estimate.
The claimed credit for Jis PFC(J) times two normal-
ization factors:
&Version normalization: the ratio of the versions
average PFC to that of the version for which average
PFC is lowest (i.e. the most efficient version).
&Host normalization: the ratio of the (host, app ver-
sion)s average PFC to the average PFC of the app
The instances of a replicated job may have different
claimed credit. BOINC computes a weighted average of
these, using a formula that reduces the impact of out-
liers, and grants this amount of credit to all the instances.
In the early days of BOINC we found that volunteers
who had accumulated credit on one project were reluc-
tant to add new projects, where theydbestartingwith
zero credit. To reduce this effect we introduced the idea
of cross-project credit: the sum of a computers, volun-
teers, or teams credit over all projects in which they
participate. This works as follows:
&Each host is assigned a unique cross-project ID. The
host ID is propagated among the projects to which
the host is attached, and a consensus algorithm
assigns a single ID.
&Each volunteer is assigned a unique cross-project
ID. Accounts with the same email address are equat-
ed. The ID is based on email address, but cannot be
used to infer it.
&Projects export (as XML files) their total and recent
average credit for hosts and volunteers, keyed by
cross-project ID.
&Cross-project statistics web sites (developed by 3rd
parties) periodically read the statistics data from all
projects, collate it based on cross-project IDs, and
display it in various forms (volunteer and host lead-
erboards, project totals, etc.).
8 Supporting Volunteers
BOINC is intended to be usable for computer owners
with little technical knowledge people who may have
never installed an application on a computer. Such peo-
ple often have questions or require help. Weve crowd-
D. P. Anderson
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
sourced technical support by creating systems where
experienced volunteers can answer questions and solve
problems for beginners. Many issues are handled via
message boards on the BOINC web site. However, these
are generally in English and dont support real-time
interaction. To address these problems we created a
system, based on Internet telephony, that connects be-
ginners with questions to helpers who speak the same
language and know about their type of computer. They
can then interact via voice or chat.
BOINC volunteers are international most countries
are represented. All of the text in the BOINC interface
both client GUIs and web site is translatable, and we
crowd-source the translation. Currently 26 languages
are supported.
Luis Sarmenta coined the term volunteer computing
[28]. The earliest VC projects (1996-97) involved prime
numbers (GIMPS) and cryptography (
The first large-scale scientific projects were
Folding@Home and SETI@home, which launched in
Several systems have been built that do volunteer
computing in a web browser: volunteers open a partic-
ular page and a Java or Javascript program runs [29].
These systems havent been widely used for various
reasons: volunteers eventually close the window, and
C and FORTRAN science applications arent supported.
In the context of BOINC, researchers have studied
the availability of volunteer computers [5,30] and their
hardware and software attributes [11].
It is difficult to study BOINCs scheduling and vali-
dation mechanisms in the field. Initially, researchers
used simulation to study client scheduling policies [25,
31], server policies [32], and entire VC systems [33]. As
the mechanisms became more complex, it was difficult
to model them accurately, and researchers began using
emulation simulators using the actual BOINC code to
model client and server behavior (we restructured the
code to make this possible). Estrada et al. developed a
system called EmBoinc that combines a simulator of a
large population of volunteer hosts (driven either by
trace data or by a random model) with an emulator of
a project server that is, the actual server software
modified to take input from the population simulator,
and to use virtual time instead of real time [26]. Our
group developed an emulator of the client [24]. Volun-
teers experience problems can upload their BOINC state
files, and run simulations, through a web-based inter-
face. This lets us debug host-specific issues without
access to the host.
Much work has been done toward integrating
BOINC with other HTC systems. Working with the
HTCondor group, we developed an adaptor that mi-
grates HTCondor jobs to a BOINC project. This is being
used by CERN [34]. Other groups have built systems
that dynamically assign jobs to different resources (lo-
cal, Grid, BOINC) based on job size and load conditions
[35]. Andrzejak et al. proposed using volunteer re-
sources as part of clouds [36]. The integration of
BOINC with the European Grid Infrastructure (EGEE)
is described by Visegradi et al. [37] and Kacsuk et al.
[38]. Bazinet and Cummings discuss strategies for
subdividing long variable-sized jobs into jobs sized
more appropriately to VC [39].
Research has been done on several of the problems
underlying volunteer computing, such as validating re-
sults [40,41,42], job runtime estimation [43], acceler-
ating the completion of batches and DAGs of jobs [44,
45], and optimizing replication policies [46,47]. Many
of these ideas could and perhaps will be implemented in
BOINC. Cano et al. surveyed the security issues inher-
ent in VC [48].
Files such as executables must be downloaded to all
the computers attached to a project. This can impose a
heavy load on the projects servers and outgoing Internet
connection. To reduce this load, Elwaer et al. [49]pro-
posed using a peer-to-peer file distribution system
(BitTorrent) to distribute such files, and implemented this
in a modified BOINC client. This method hasnt been
used by any projects, but it may be useful in the future.
Researchers have explored how existing distributed
computing paradigms and algorithms can be adapted to
VC. Examples include evolutionary algorithms [50,51],
virtual drug discovery [52], MPI-style distributed paral-
lel programming [53], Map-Reduce computing [54],
data analytics [55], machine learning [56,57,58], gene
sequence alignment [59], climate modeling [21], and
distributed sensor networks [60]. Desell et al. describe
a system that combines VC and participatory citizen
Silva et al. proposed extending BOINC to allow job
submission by volunteers [62]. Marosi et al. proposed a
cloud-like system based on BOINC with extensions
developed at SZTAKI [19].
BOINC: A Platform for Volunteer Computing 117
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Binti et al. studied a job dispatch policy designed to
minimize air-conditioning costs in campus grids [63].
10 Conclusion and Future Work
Volunteer computing can provide high-throughput com-
puting power in larger quantities and at lower costs than
computing centers and commercial clouds. BOINC pro-
vides a flexible open-source platform for volunteer com-
puting. Section 1outlined the challenges that arise in
using untrusted, unreliable, and highly heterogeneous
computing resources for HTC. In BOINC, we have
developed mechanisms that effectively address each of
these challenges, thus transforming a global pool of
consumer devices into a reliable computing resource
for a wide range of scientific applications.
In addition to summarizing the features, architec-
ture, and implementation of BOINC, we have present-
ed detailed descriptions of the algorithms and policies
it uses, some of which are novel. These mechanisms
have been developed in response to the demands of
real-world, operational VC projects. They have satis-
fied these demands, thus showing their effectiveness.
We have not, for the most part, formally studied the
performance of the mechanisms, or compared them
with alternatives. We hope that this paper enables
studies of this sort.
We now describe current and prospective future work
aimed at extending the use of BOINC.
10.1 Coordinated Volunteer Computing
The original BOINC participation model was intended
to produce a dynamic and growing ecosystem of pro-
jects and volunteers. This has not happened: the set of
projects has been mostly static, and the volunteer pop-
ulation has gradually declined. The reasons are likely
inherent in the model. Creating a BOINC project is
risky: its a significant investment, with no guarantee
of any volunteers, and hence of any computing power.
Publicizing VC in general is difficult because each
project is a separate brand, presenting a diluted and
confusing image to the public. Volunteers tend to stick
with the same projects, so its difficult for new projects
to get volunteers.
To address these problems, we created a new co-
ordinated model for VC. This involves an account
manager, Science United (SU), in which volunteers
register for scientific areas (using the keyword mech-
anism described in Section 2.4)ratherthanforspecif-
ic projects [64]. SU dynamically attaches hosts to
projects based on these science preferences. Projects
are vetted by SU, and SU has a mechanism (based on
the linear-bounded model described in Section 3.7)
for allocating computing power among projects. This
means that a prospective new project can be guaran-
teed a certain amount of computing power before any
investment is made, thus reducing the risk involved in
creating a new project.
10.2 Integration with Existing HTC Systems
While the coordinated model reduces barriers to project
creation, the barriers are still too high for most scientists.
To address this, we are exploring the use of VC by
existing HTC providers like computing centers and
science gateways. Selected jobs in such systems will
be handled by VC, thus increasing the capacity available
to the HTC providers clients, which might number
thousands of scientists. We are prototyping this type of
facility at Texas Advanced Computing Center (TACC)
and at nanoHUB, a gateway for nanoscience [65]. Both
projects use Docker and a universal VM app to handle
application porting.
10.3 Data Archival on Volunteer Devices
Host churn means that a file stored on a host may
disappear forever at any moment. This makes it chal-
lenging to use BOINC for high-reliability data archival.
However, its possible. Encoding (e.g. Reed-Solomon)
reconstructed even if a number of hosts disappear. This
can be used to achieve arbitrarily high levels of reliabil-
ity with reasonable space overhead. However,
reconstructing the file requires uploading the remaining
chunks to a server. This imposes high network over-
head, and it requires that the original file fit on the
server. To solve these problems, we have developed
and implemented a technique called multi-level
encodingin which the chunks from a top-level
encoding are further encoded into smaller 2nd-level
chunks. When a failure occurs, generally only a top-
level chunk needs to be reconstructed, rather than the
entire file.
D. P. Anderson
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10.4 Using More Types of Devices
Ideally, BOINC should be available for any consumer
device thats networked and can run scientific jobs. This
is not yet the case. BOINC runs on Android but not
Apple iOS, because Apples rules bar apps from dynam-
ically downloading executable code. It also is not avail-
able for video game consoles or Internet of Things
devices such as appliances. The barriers are generally
not technical, but rather that the cooperation of the
device manufacturers is needed.
In addition, although BOINC on Android detects on-
chip GPUs via OpenCL, no projects currently have apps
that use them. Its likely that their use will raise issues
involving heat and power.
10.5 Secure Computing
Recent processor architectures include features that al-
low programs to run in secure enclavesthat cannot be
accessed by the computer owner. Examples include
Intels Software Guard Extensions (SGX), AMD Secure
Encrypted Virtualization, ARMs TrustZone, and the
proposed Trusted Platform Module [66]. Extending
BOINC to use such systems has two possible advan-
tages: a) it could eliminate the need for result validation,
and b) it would make it possible to run applications
whose data is sensitive for example, commercial drug
discovery, and medical research applications involving
patient data.
10.6 Recruiting and Retaining Volunteers
Early volunteer computing projects such as
SETI@home received mass media coverage in 1999-
2001, and attracted on the order of 1 M volunteers. Since
then there has been little media coverage, and the user
base has shrunk to 200 K or so. Surveys have shown that
most volunteers are male, middle-aged, IT professionals
Several technology companies have promoted VC by
developing and publicizing brandedversions of the
BOINC client. Examples include Intel (Progress thru
Processors), HTC (Power to Give) and Samsung (Power
Sleep). None of these resulted in a significant long-term
increase in volunteers.
Volunteer computing faces the challenge of market-
ing itself to the broader public. Possible approaches
include social media, public service announcements,
and partnerships with companies such as video game
frameworks, device manufacturers, and cell phone ser-
vice providers.
Another idea is to use BOINC-based computing as
proof-of-work for virtual currency and other blockchain
systems, and use the tokens issued by these systems as a
reward for participating. One such system exists [69],
and others are under development.
10.7 Scheduling
There are a number of possible extensions and improve-
ments related to scheduling; some of these are explored
by Chernov et al. [70]. Dinis et al. proposed facilitating
experimentation by making the server scheduler
BOINC currently has no direct support for directed
acyclic graphs (DAGs) of dependent jobs. BOINC
doesnt currently support low-latency computing, by
which we mean the ability to complete a job or DAG
of jobs as fast as possible, and to give an estimate of
completion time in advance. Doing so would require a
reworking of all the scheduling mechanisms. For exam-
ple, job runtime estimation would need to use sample
variance as well as sample mean, in order to bound the
probability of deadline miss. Job deadlines would be
assigned dynamically during job dispatch, rather than
specified by the submitter. Replication could be used to
reduce expected latency.
When using host availability as part of job runtime
estimation, BOINC uses the average availability.
This doesnt take into account the possibility that
unavailability occurs on large chunks, or that it oc-
curs with a daily or weekly periodicity. Doing so
could provide more accurate, and in some cases low-
er, estimates.
To accelerate the completion of batches (and perhaps
DAGs), BOINC could add a mechanism to deal with the
straggler effect: the tendency for a few jobs to delay the
completion of the batch as a whole. The idea is send
these jobs to fast, reliable, and available computers, and
possibly to replicate the jobs. These ideas have been
explored by others [44].
Although individual volunteered computers are un-
predictable, the behavior of a large pool of such com-
puters is fairly predictable. Hence BOINC could poten-
tially provide quality-of-service guarantees, such as a
level of throughput over a time interval, to projects or
job submitters within a project.
BOINC: A Platform for Volunteer Computing 119
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Acknowledgements Many people contributed to the ideas pre-
sented here and to their implementation. Thanks in particular to
Bruce Allen, Nicolás Alvarez, Matt Blumberg, Karl Chen, Carl
Christensen, Charlie Fenton, David Gedye, Francois Grey, Eric
Korpela, Janus Kristensen, John McLeod VII, Kevin Reed, Ben
Segal, Michela Taufer, and Rom Walton. The National Science
Foundation has supported BOINC with several grants, most re-
cently award #1664190.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://, which permits unrestrict-
ed use, distribution, and reproduction in any medium, provided
you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if
changes were made.
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... Research on volunteer distributed computing models is multifaceted and extensively studied. Some references from these studies ranging from volunteer computing architectures [7,8,13], resource discovery [14,15], resource allocation [16,17], security [18], and energy efficiency [19,20]. However, herein we discuss some of the recent related work on incentive mechanisms in distributed ad hoc environments. ...
... Figure 12 presents the ethers received by the broker as the 2% escrow fee deductions from all of the escrows in each epoch. In Fig. 12, the y-axis presents the ether amount (3,23,29,19,17) RP 5 (26,33,10,4,30) RP 15 (11,30,5,13,33) RP 25 (20,25,9,11,23) RP 6 (31,24,5,15,16) RP 16 (22,23,31,34,3) RP 26 (12,33,30,24,23) RP 7 (31,26,22,5,2) RP 17 (4,30,23,17,10) RP 27 (22,4,18,20,31) RP 8 (10,2,17,12,21) RP 18 (24,6,13,23,17) RP 28 (15,26,29,22,6) RP 9 (24,29,6,24,5) RP 19 (5,30,10,2,29) RP 29 (21,10,28,25,12) RP 10 (21,4,31,16,25) RP 20 (31,5,9,23,13) RP 30 (13,22,5,23,9) and the x-axis presents the epoch. In total, the broker received around 1982 ethers in ten epochs. ...
... Figure 12 presents the ethers received by the broker as the 2% escrow fee deductions from all of the escrows in each epoch. In Fig. 12, the y-axis presents the ether amount (3,23,29,19,17) RP 5 (26,33,10,4,30) RP 15 (11,30,5,13,33) RP 25 (20,25,9,11,23) RP 6 (31,24,5,15,16) RP 16 (22,23,31,34,3) RP 26 (12,33,30,24,23) RP 7 (31,26,22,5,2) RP 17 (4,30,23,17,10) RP 27 (22,4,18,20,31) RP 8 (10,2,17,12,21) RP 18 (24,6,13,23,17) RP 28 (15,26,29,22,6) RP 9 (24,29,6,24,5) RP 19 (5,30,10,2,29) RP 29 (21,10,28,25,12) RP 10 (21,4,31,16,25) RP 20 (31,5,9,23,13) RP 30 (13,22,5,23,9) and the x-axis presents the epoch. In total, the broker received around 1982 ethers in ten epochs. ...
Full-text available
Desktop clouds connect several desktop computers into a cloud computing architecture to reap the potential of untapped commodity computing power over the Internet. In desktop clouds, what benefit (incentive) a participant will get for sharing its computational resources, and how participants will contribute (pay) after consuming computational resources from other participants. This inexistence of monetary incentives hinders the widespread adoption of desktop clouds as there is no motivation for the participants to join and remain in the desktop cloud environment. In this article, we propose a decentralized escrow approach over the ethereum blockchain for enhancing the expectation of a participating node to join and offer services in desktop cloud networks. We then propose a distributed multi-agent framework for desktop cloud environments. Moreover, we present the agents’ full algorithmic behavior with their interaction to the escrow over the ethereum smart contract. The proposed framework provides monetary incentives using blockchain-based cryptocurrencies managed through decentralized escrow over ethereum smart contract to the desktop cloud participants in a trusted manner. Lastly, we present simulation results from a testbed verifying the monetization of desktop cloud participants in the proposed framework.
... The work unit consists of multiple tasks or each work unit is independent when the task is sent from the server to the client for processing (e.g., data handling). The work unit consists of executable code, parameters, and input files, which are sent to the server after processing [8][9][10][11]. However, the inter-institutional-CC model is represented in the cloud model, which is using an IaaS type and allows idle computer cycles to contribute and requires higher levels in opportunistic resources environments. ...
... The larger the loss on the validation set, the less the impact of the global model on the client model. The loss function of customer I in formula (7) can be refined as follows: and then the local weights of the client can be defined as shown in Eq. (8). Algorithm 1 illustrates the collaborative learning framework with the teacher-student mechanism and joint identification-verification. ...
... Because the request is dynamic, we can't always rely on a minimal machine alone. To deliver a high-quality service, other machines must be employed [18]. The issue of allocation of resources is cost prohibitive, and it necessitates some hypotheses, in which main purpose to stop the wastage of energy that fulfil the requirement of service level agreement by proper distribution of tasks in efficient way. ...
Full-text available
Distributed computing, which utilizes a pay more only as costs arise model is the most broadly received IT framework model by a large portion of the customer currently. For cloud based data center, energy proficiency is a challenging issue. The vast majority of the Cloud suppliers are offering the types of assistance through their Service Level Agreement (SLA) predominantly not in Quality of Service gave. Be that as it may not every one of the machines are 100% used. The information base of somewhat similar from various customers can be observed, recorded and this data can be viably used to oversee and rearrange the administrations advertised. SLA portfolio-based model is viably used the Cloud. For green cloud environment that is viable by using an Energy efficient Pseudocode to choose appropriate computer-generated services and disseminate the power consumption resources at the client, server, and network side.
... However, the shared resources inside the virtual organization are inherently unreliable, volatile, and not always accessible. Additionally, computing resources are geographically distributed across numerous sites and are managed by separate independent resource providers with varying objectives, priorities, and management policies within a VO [7]. As a consequence, the application running time or completion time (makspan) of a job can be typically hours or even for days, which makes a substantial impact on the reliability of distributed systems, since reliability is often measured in terms of the probability that a job will be successfully completed by the system [1]. ...
Full-text available
Scientific applications often require substantial amount of computing resources for running challenging jobs potentially consisting of many tasks from hundreds of thousands to even millions. As a result, many institutions collaborate to solve large-scale problems by creating virtual organizations (VOs), and integrate hundreds of thousands of geographically distributed heterogeneous computing resources. Over the past decade, VOs have been proven to be a powerful research testbed for accessing massive amount of computing resources shared by several organizations at almost no cost. However, VOs often suffer from providing exact dynamic resource information due to their scale and autonomous resource management policies. Furthermore, shared resources are inconsistent, making it difficult to accurately forecast resource capacity. An effective VO’s resource profiling and modeling system can address these problems by forecasting resource characteristics and availability. This paper presents effective resource profiling and performance prediction models including Adaptive Filter-based Online Linear Regression (AFOLR) and Adaptive Filter-based Moving Average (AFMV) based on the linear difference equation combining past predicted values and recent profiled information, which aim to support large-scale applications in distributed scientific computing environments. We performed quantitative analysis and conducted microbenchmark experiments on a real multinational shared computing platform. Our evaluation results demonstrate that the proposed prediction schemes outperform well-known common approaches in terms of accuracy, and actually can help users in a shared resource environment to run their large-scale applications by effectively forecasting various computing resource capacity and performance.
... The challenges are detailed in section 2.1. We can see FEEL as a type of Volunteer Computing (VC) [1,9, 14,17,30] distributed system where users, and devices (i.e., volunteers), with local and private datasets voluntarily donate their computing resources to a project and collaborate to train a shared ML model. ...
Full-text available
The number of devices, from smartphones to IoT hardware, interconnected via the Internet is growing all the time. These devices produce a large amount of data that cannot be analyzed in any data center or stored in the cloud, and it might be private or sensitive, thus precluding existing classic approaches. However, studying these data and gaining insights from them is still of great relevance to science and society. Recently, two related paradigms try to address the above problems. On the one hand, edge computing (EC) suggests to increase processing on edge devices. On the other hand, federated learning (FL) deals with training a shared machine learning (ML) model in a distributed (non-centralized) manner while keeping private data locally on edge devices. The combination of both is known as federated edge learning (FEEL). In this work, we propose an algorithm for FEEL that adapts to asynchronous clients joining and leaving the computation. Our research focuses on adapting the learning when the number of volunteers is low and may even drop to zero. We propose, implement, and evaluate a new software platform for this purpose. We then evaluate its results on problems relevant to FEEL. The proposed decentralized and adaptive system architecture for asynchronous learning allows volunteer users to yield their device resources and local data to train a shared ML model. The platform dynamically self-adapts to variations in the number of collaborating heterogeneous devices due to unexpected disconnections (i.e., volunteers can join and leave at any time). Thus, we conduct comprehensive empirical analysis in a static configuration and highly dynamic and changing scenarios. The public open-source platform enables interoperability between volunteers connected using web browsers and Python processes. We show that our platform adapts well to the changing environment getting a numerical accuracy similar to today’s configurations using a given number of homogeneous (hardware and software) computers as a static platform for learning. We demonstrate the fault-tolerance of the platform in self-recovering from unexpected disconnections of volunteer devices. We then prove that EC, coupled with FL, can lead to scientific tools that can be practical involving real users for final competitive numerical results in real problems for science and society.
... The current dynamic era certainly necessitates to fully utilize effectively the computational resources, despite prevalent and exponentially growing computing infrastructures such as data centers loaded with thousands of powerful servers, workstations incremental computational capacity, and the proliferation and prevalence of smart mobile devices [5]. Volunteer computing (VC) is simply a form of network-based distributed computing provisioned to aggregate idle computing resources of surrounding volunteer devices. ...
Full-text available
Volunteer Computing provision of seamless connectivity that enables convenient and rapid deployment of greener and cheaper computing infrastructure is extremely promising to complement next-generation distributed computing systems. Undoubtedly, without tactile Internet and secure VC ecosystems, harnessing its full potentials and making it an alternative viable and reliable computing infrastructure is next to impossible. Android-enabled smart devices, applications, and services are inevitable for Volunteer computing. Contrarily, the progressive developments of sophisticated Android malware may reduce its exponential growth. Besides, Android malwares are considered the most potential and persistent cyber threat to mobile VC systems. To secure Android-based mobile volunteer computing, the authors proposed MulDroid, an efficient and self-learning autonomous hybrid (Long-Short-Term Memory, Convolutional Neural Network, Deep Neural Network) multi-vector Android malware threat detection framework. The proposed mechanism is highly scalable with well-coordinated infrastructure and self-optimizing capabilities to proficiently tackle fast-growing dynamic variants of sophisticated malware threats and attacks with 99.01% detection accuracy. For a comprehensive evaluation, the authors employed current state-of-the-art malware datasets (Android Malware Dataset, Androzoo) with standard performance evaluation metrics. Moreover, MulDroid is compared with our constructed contemporary hybrid DL-driven architectures and benchmark algorithms. Our proposed mechanism outperforms in terms of detection accuracy with a trivial tradeoff speed efficiency. Additionally, a 10-fold cross-validation is performed to explicitly show unbiased results.
The main goal of the work was aimed to create a parallel application using a multithreaded execution model, which will allow the most complete and efficient use of all available computing resources. At the same time, the main attention was paid to the issues of maximizing the performance of the multithreaded computing part of the application and more efficient use of available hardware. During the development process, the effectiveness of various methods of software and algorithmic optimization was evaluated, taking into account the features of the functioning of a highly loaded multithreaded application, designed to run on systems with a large number of parallel computing threads. The problem of loading all available computing resources at the moment was solved, including the dynamic distribution of the involved CPU cores/threads and the computing accelerators, installed in the system.
In this paper a short survey on Desktop Grid and Cloud computing paradigms combination is presented. There are four approaches are considered: (1) extension of a Cloud by Desktop Grid’s nodes; (2) extension of a Desktop Grid by a Cloud nodes; (3) implementation of Cloud services on top of Desktop Grid’s nodes; and (4) implementation of a Desktop Grid as a Service.
A large number of personal mobile devices and the presence of powerful computing processors on them allows one to use them as computing nodes. The idle computing power of mobile devices can be used in distributed computing systems. This paper discusses the use of personal mobile devices in the project of voluntary distributed computing. The features of computing on mobile devices are discussed. The results of computational experiments on a desktop grid system are presented. Setting up a BOINC-based desktop grid system for efficient use of mobile devices as computing nodes is discussed.
Conference Paper
Full-text available
We present a comparative analysis of the maximum performance achieved by the Linpack benchmark on compute intensive hardware publicly available from multiple cloud providers. We study both performance within a single compute node, and speedup for distributed memory calculations with up to 32 nodes or at least 512 computing cores. We distinguish between hyper-threaded and non-hyper-threaded scenarios and estimate the performance per single computing core. We also compare results with a traditional supercomputing system for reference. Our findings provide a way to rank the cloud providers and demonstrate the viability of the cloud for high performance computing applications.
Full-text available
The LHC@Home BOINC project has provided computing capacity for numerical simulations to researchers at CERN since 2004, and has since 2011 been expanded with a wider range of applications. The traditional CERN accelerator physics simulation code SixTrack enjoys continuing volunteers support, and thanks to virtualisation a number of applications from the LHC experiment collaborations and particle theory groups have joined the consolidated LHC@Home BOINC project. This paper addresses the challenges related to traditional and virtualized applications in the BOINC environment, and how volunteer computing has been integrated into the overall computing strategy of the laboratory through the consolidated LHC@Home service. Thanks to the computing power provided by volunteers joining LHC@Home, numerous accelerator beam physics studies have been carried out, yielding an improved understanding of charged particle dynamics in the CERN Large Hadron Collider (LHC) and its future upgrades. The main results are highlighted in this paper.
Full-text available
We survey the areas of Desktop Grid task scheduling that seem to be insufficiently studied so far and are promising for efficiency, reliability, and quality of Desktop Grid computing. These topics include optimal task grouping, “needle in a haystack” paradigm, game-theoretical scheduling, domain-imposed approaches, special optimization of the final stage of the batch computation, and Enterprise Desktop Grids.
Conference Paper
Full-text available
Virtual drug screening is one of the most common applications of high-throughput computing. As virtual screening is time consuming, a problem of obtaining a diverse set of hits in a short time is very important. We propose a mathematical model based on game theory. Task scheduling for virtual drug screening in high-performance computational systems is considered as a congestion game between computing nodes to find the equilibrium solutions for best balancing between the number of interim hits and their chemical diversity. We present the developed scheduling algorithm implementation for Desktop Grid and Enterprise Desktop Grid, and perform comprehensive computational experiments to evaluate its performance. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hits discovery rate and chemical diversity at earlier steps.
Full-text available
Computers connected to internet represent an immense computing power, mostly unused by their owners. One way to utilize this public resource is via world wide web, where users can share their resources using nothing more except their browsers. We survey the techniques employing the idea of browser-based voluntary computing (BBVC), discuss their commonalities, recognize recurring problems and their solutions and finally we describe a prototype implementation aiming at efficient mining of voluntary-contributed computing power.
Full-text available
We present a comparative analysis of the maximum performance achieved by the Linpack benchmark on compute intensive hardware publicly available from multiple cloud providers. We study both performance within a single compute node, and speedup for distributed memory calculations with up to 32 nodes or at least 512 computing cores. We distinguish between hyper-threaded and non-hyper-threaded scenarios and estimate the performance per single computing core. We also compare results with a traditional supercomputing system for reference. Our findings provide a way to rank the cloud providers and demonstrate the viability of the cloud for high performance computing applications.
Conference Paper
Full-text available
Trust has various instantiations: some rely on real-world relationships between entities, while others depend on robust hardware and software technologies to establish it post-deployment. In this paper, we focus on the latter, analyse their evolution in previous years, and their scope in the near future. The evolution of such technologies has involved diverse approaches; consequently, trust is understood and ascertained differently across heterogeneous systems and domains. We look at trusted hardware and software technologies from a security perspective – revisiting and analysing the Trusted Platform Module (TPM); Secure Elements (SE); hypervisors and virtualisation, including Java Card and Intel's Trusted eXecution Technology (TXT); Trusted Execution Environments (TEEs), such as GlobalPlatform TEE and Intel SGX; Host Card Emulation (HCE); and the Encrypted Execution Environment (E3). In our analysis, we focus on these technologies and their application to the emerging domains of the Internet of Things (IoT) and Cyber-Physical Systems (CPS).
Conference Paper
This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multithreaded and MPI versions, EXACT has been implemented as part of a BOINC volunteer computing project, allowing large scale evolution. During a period of two months, over 4,500 volunteered computers on the Citizen Science Grid trained over 120,000 CNNs and evolved networks reaching 98.32% test data accuracy on the MNIST handwritten digits dataset. These results are even stronger as the backpropagation strategy used to train the CNNs was fairly rudimentary (ReLU units, L2 regularization and Nesterov momentum) and these were initial test runs done without refinement of the backpropagation hyperparameters. Further, the EXACT evolutionary strategy is independent of the method used to train the CNNs, so they could be further improved by advanced techniques like elastic distortions, pretraining and dropout. The evolved networks are also quite interesting, showing "organic" structures and significant differences from standard human designed architectures.